class_docstr: The default implementation of `DdpAccelerator`, which is just a thin wrapper
  around PyTorch's `DistributedDataParallel`.
code: def wrap_model(
      self, model: "Model"
  ) -> Tuple["Model", DdpWrappedModel]:
      if self.cuda_device != torch.device("cpu"):
          model = model.cuda(self.cuda_device)
      wrapped_model = torch.nn.parallel.DistributedDataParallel(
          model,
          device_ids=None
          if self.cuda_device == torch.device("cpu")
          else [self.cuda_device],
          **self._ddp_kwargs,
      )
      # Add hooks to remove the 'module.' prefix from all keys in the state dict returned
      # from the DistrubtedDataParallel model's `state_dict()` method,
      # and add it back on when loading a state dict.
      wrapped_model._register_state_dict_hook(
          TorchDdpAccelerator._remove_torch_ddp_prefix
      )
      wrapped_model._register_load_state_dict_pre_hook(
          TorchDdpAccelerator._add_torch_ddp_prefix
      )
      return model, DdpWrappedModel(
          wrapped_model,
          local_rank=self.local_rank,
          world_size=self.world_size,
      )
  
imports: import os
  from typing import Union, Tuple, OrderedDict, Dict, NamedTuple, List, Optional, Any, Sequence, TYPE_CHECKING
  import torch
  import torch.distributed
  from torch.cuda import amp
  from torch.nn.utils import clip_grad_norm_
  from allennlp.common import Registrable
  from allennlp.common.util import int_to_device
  from allennlp.nn.parallel.sharded_module_mixin import ShardedModuleMixin
label_desc: Use image node_id 2 for calling the TorchDdpAccelerator obj's underlying member method code with example usage: obj.wrap_model(model) and returns: model,  DdpWrappedModel